Founded in 1999, aWhere collects and analyzes over a billion points of data from around the world on a daily basis to create unprecedented visibility and insights for field-level to global decisions. Agricultural intelligence is a key component for anyone working in the agricultural supply chain, as weather and agronomic information plays a critical role in crop management and production.

aWhere combines high-quality weather data designed purely for agricultural use with scientifically vetted agronomic models. The data includes long term historical daily data, current weather and up to 15 days of hourly forecasts. With weather patterns changing, this type of big data insight is required more and more, to informed agricultural business decisions.

Data can do a lot to help make decisions, but not all data is created equal. Knowing how to select the right inputs, how to interpret it in the right context, and what signals matter and which don’t is all key to ensuring the data is actually meaningful for the audience. Weather data may seem basic, but when it comes to agriculture it’s crucial:

• Weather variability is increasing and throwing off traditional models and farming practices around the world. A data feed or radar map alone doesn’t help a farmer know what to do this season, this week, or today.

• Placing emphasis on the right data means deriving more relevant insights, and that means knowing what plants actually need and how farming actually works.

• Understanding the right inputs to use ensures the data is relevant for agriculture; a weather station at the nearest airport is irrelevant for a field 50 miles away.

• Context is key—plants have different needs so knowing what’s planted in a field means interpreting the data to make better decisions. aWhere uses multiple sources of weather data for every location.

No single source of raw data is reliable enough or sufficient to paint a complete picture of real, on-the-ground conditions. Ground stations only represent the immediate local area, but cannot identify what happens in the reaches of space between them. Doppler radar becomes much less useful for agricultural purposes beyond 75km. Even satellites, constantly on the move, can have gaps in available data. Any weather data provider who predominantly relies on a single technology cannot paint a picture reliable enough to drive important food production and trading decisions.

Our data center continuously imports and processes multiple high-quality sources of raw meteorological data—never relying on just one source for any location. Our ground station network spans the globe, and only includes agriculturally-relevant weather stations (those in fields or away from cities). Layered on top of that is Doppler radar and both public and aWhere-exclusive satellite data. Our proprietary algorithms select the most appropriate sources and logic for each location, and we do all this with the same approach, quality, and resolution all around the globe.

The Problem with Single-Source Solutions

Many providers rely heavily on a single source of data, but no single source is infallible. For example, Doppler radar is an excellent technology but does not paint a complete picture. Figure 1 shows the supposed coverage of all Doppler radar in the US, about 150km radiuses from each station. But this map ignores the curvature of the Earth, and so as you get farther from a radar station, the beam moves further away from the ground. This is fine for tracking large storm systems, but becomes very unreliable for regular rain that might hit a f field. Figure 2 shows the effective range of Doppler for agricultural purposes, or about 75km radiuses, leaving a lot of farm land uncovered by effective radar data.